scholarly journals B 1 inhomogeneity correction of RARE MRI at low SNR: Quantitative in vivo 19 F MRI of mouse neuroinflammation with a cryogenically‐cooled transceive surface radiofrequency probe

2021 ◽  
Author(s):  
Paula Ramos Delgado ◽  
Andre Kuehne ◽  
Mariya Aravina ◽  
Jason M. Millward ◽  
Alonso Vázquez ◽  
...  
Keyword(s):  
Low Snr ◽  
Inhomogeneity Correction

Volume and surface coil simultaneous reception (VSSR) method for intensity inhomogeneity correction in MRI

2021 ◽  
pp. 1-12
Author(s):  
Lin Wu ◽  
Tian He ◽  
Jie Yu ◽  
Hang Liu ◽  
Shuang Zhang ◽  
...  
Keyword(s):  
Bias Field ◽  
Correction Method ◽  
Surface Coil ◽  
Intensity Inhomogeneity ◽  
Acquisition Time ◽  
Surface Coils ◽  
In Vivo Experiments ◽  
Inhomogeneity Correction ◽  
Volume Coil

BACKGROUND: Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time. OBJECTIVE: In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR). METHODS: The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods. RESULTS: Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil. CONCLUSION: The VSSR may be considered suitable for general implementation.

scholarly journals Navigator-Free Submillimeter Diffusion Imaging Using Multishot-Encoded Simultaneous Multi-Slice (MUSIUM)

2020 ◽  
Author(s):  
Wei-Tang Chang ◽  
Khoi Huynh ◽  
Pew-Thian Yap ◽  
Weili Lin
Keyword(s):  
Signal To Noise Ratio ◽  
Diffusion Imaging ◽  
Brain Structures ◽  
Peak Amplitude ◽  
Acquisition Time ◽  
Rf Power ◽  
Isotropic Resolution ◽  
Low Snr ◽  
Scan Time

Abstract The ability to achieve submillimter isotropic resolution diffusion MR imaging (dMRI) is critically important to study fine-scale brain structures. One of the major challenges in submillimeter dMRI is the inherently low signal-to-noise ratio (SNR). While approaches capable of mitigating the low SNR have been proposed, namely simultaneous multi-slab (SMSlab) and generalized slice dithered enhanced resolution with simultaneous multislice (gSlider-SMS), limitations are associated with these approaches. The SMSlab sequences suffer from the slab boundary artifacts and require additional navigators for phase estimation. On the other hand, gSlider sequences require relatively high RF power and peak amplitude, which increase the SAR and complicate the RF excitation. In this work, we developed a navigator-free multishot-encoded simultaneous multi-slice (MUSIUM) imaging approach, achieving enhanced SNR, low RF power and peak amplitude, and being free from slab boundary artifacts. The dMRI with ultrahigh resolution (0.86 mm isotropic), whole brain coverage and ~12.5 minute acquisition time were achieved, revealing detailed structures at cortical and white matter areas. The simulated and in vivo results also demonstrated that the MUSIUM imaging was minimally affected by the motion. Taken together, the MUSIUM imaging is a promising approach to achieve submillimeter diffusion imaging on 3T scanner within clinically feasible scan time.

scholarly journals Uncertainty in denoising of MRSI using low-rank methods

2021 ◽  
Author(s):  
William T Clarke ◽  
Mark Chiew
Keyword(s):  
Linear Prediction ◽  
Low Rank ◽  
Threshold Selection ◽  
Low Snr ◽  
Uncertainty Estimates ◽  
Spatio Temporal ◽  
Downstream Analysis ◽  
The Right ◽  
Mrsi Data

Purpose: Low-rank denoising of MRSI data results in an apparent increase in spectral SNR. However, it is not clear if this translates to a lower uncertainty in metabolite concentrations after spectroscopic fitting. Estimation of the true uncertainty after denoising is desirable for downstream analysis in spectroscopy. In this work the uncertainty reduction from low-rank denoising methods based on spatio-temporal separability and linear predictability in MRSI are assessed. A new method for estimating metabolite concentration uncertainty after denoising is proposed. Finally, automatic rank threshold selection methods are assessed in simulated low SNR regimes. Methods: Assessment of denoising methods is conducted using Monte Carlo simulation of proton MRSI data, and by reproducibility of repeated in vivo acquisitions in five subjects. Results: In simulated and in vivo data, spatio-temporal based denoising is shown to reduce the concentration uncertainty, but linear prediction denoising increases uncertainty. Uncertainty estimates provided by fitting algorithms after denoising consistently under-estimate actual metabolite uncertainty. However, the proposed uncertainty estimation, based on an analytical expression for entry-wise variance after denoising, is more accurate. Finally, it is shown automated rank threshold selection using Marchenko-Pastur distribution can bias the data in low SNR conditions. An alternative soft-thresholding function is proposed. Conclusion: Low-rank denoising methods based on spatio-temporal separability do reduce uncertainty in MRS(I) data. However, thorough assessment is needed as assessment by SNR measured from residual baseline noise is insufficient given the presence of non-uniform variance. It is also important to select the right rank thresholding method in low SNR cases.

scholarly journals Three-dimensional vasculature reconstruction of tumour microenvironment via local clustering and classification

2013 ◽  
Vol 3 (4) ◽  
pp. 20130015 ◽  
Author(s):  
Yanqiao Zhu ◽  
Fuhai Li ◽  
Tegy J. Vadakkan ◽  
Mei Zhang ◽  
John Landua ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Three Dimensional ◽  
Tumour Microenvironment ◽  
Normal Mammary Gland ◽  
Low Snr ◽  
Mammary Tumours ◽  
Local Clustering ◽  
Clustering And Classification ◽  
Three Dimensional Images

The vasculature inside breast cancers is one important component of the tumour microenvironment. The investigation of its spatial morphology, distribution and interactions with cancer cells, including cancer stem cells, is essential for elucidating mechanisms of tumour development and treatment response. Using confocal microscopy and fluorescent markers, we have acquired three-dimensional images of vasculature within mammary tumours and normal mammary gland of mouse models. However, it is difficult to segment and reconstruct complex vasculature accurately from the in vivo three-dimensional images owing to the existence of uneven intensity and regions with low signal-to-noise ratios (SNR). To overcome these challenges, we have developed a novel three-dimensional vasculature segmentation method based on local clustering and classification. First, images of vasculature are clustered into local regions, whose boundaries well delineate vasculature even in low SNR and uneven intensity regions. Then local regions belonging to vasculature are identified by applying a semi-supervised classification method based on three informative features of the local regions. Comparison of results using simulated and real vasculature images, from mouse mammary tumours and normal mammary gland, shows that the new method outperforms existing methods, and can be used for three-dimensional images with uneven background and low SNR to achieve accurate vasculature reconstruction.

scholarly journals Accuracy and precision in super-resolution MRI: Enabling spherical tensor diffusion encoding at ultra-high b-values and high resolution

2021 ◽  
Author(s):  
Geraline Vis ◽  
Markus Nilsson ◽  
Carl-Fredrik Westin ◽  
Filip Szczepankiewicz
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Signal Acquisition ◽  
Noise Floor ◽  
B Values ◽  
Low Snr ◽  
Accuracy And Precision ◽  
Signal Accuracy

Diffusion MRI (dMRI) is a useful probe of tissue microstructure but suffers from low signal-to-noise ratio (SNR) whenever high resolution and/or high diffusion encoding strengths are used. Low SNR leads not only to poor precision but also poor accuracy of the diffusion-weighted signal, as the rectified noise floor gives rise to a positive signal bias. Recently, super-resolution techniques have been proposed for signal acquisition at a low spatial resolution but high SNR, whereafter a higher spatial resolution is recovered by image reconstruction. In this work, we describe a super-resolution reconstruction framework for dMRI and investigate its performance with respect to signal accuracy and precision. Using strictly controlled phantom experiments, we show that the super-resolution approach improves accuracy by facilitating a more beneficial trade-off between spatial resolution and diffusion encoding strength before the noise floor affects the signal. Moreover, precision is shown to have a less straightforward dependency on acquisition, reconstruction, and intrinsic tissue parameters. Indeed, we find that a gain in precision from super-resolution reconstruction (SRR) is substantial only when some spatial resolution is sacrificed. We also demonstrated the value of SRR in the challenging combination of high resolution and spherical b-tensor encoding at ultrahigh b-values -- a configuration that produces a unique contrast that emphasizes tissue in which diffusion is restricted in all directions. We conclude that SRR is most valuable in low-SNR conditions, where it can suppress rectified noise floor effects and recover signal with high accuracy. The in vivo application showcases a vastly superior image contrast when using SRR compared to conventional imaging, facilitating investigations of brain tissue that would otherwise have prohibitively low SNR, resolution or required non-conventional MRI hardware.

scholarly journals ICoRD: Iterative Correlation-Based ROI Detection Method for the Extraction of Neural Signals in Calcium Imaging

2021 ◽  
Author(s):  
Seongtak Kang ◽  
Jiho Park ◽  
Kyungsoo Kim ◽  
Sung-Ho Lim ◽  
Joon Ho Choi ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Neural Circuit ◽  
Detection Methods ◽  
Calcium Signal ◽  
Noise Power ◽  
Imaging Data ◽  
Calcium Signals ◽  
Low Snr ◽  
Target Neuron

In vivo calcium imaging is a standard neuroimaging technique that allows the simultaneous observation of neuronal population activity. In calcium imaging, the activation signals of neurons are key information for the investigation of neural circuits. For efficient extraction of the calcium signals of neurons, selective detection of the region of interest (ROI) pixels corresponding to the active subcellular region of the target neuron is essential. However, current ROI detection methods for calcium imaging data exhibit relatively low extraction performance from neurons with a low signal-to-noise power ratio (SNR). This is problematic because a low SNR is unavoidable in many biological experimental settings. Therefore, we propose an iterative correlation-based ROI detection (ICoRD) method that robustly extracts the calcium signal of the target neuron from a calcium imaging series with severe noise. ICoRD extracts calcium signals closer to the ground truth than the conventional method from simulated calcium imaging data in all low SNR ranges. Additionally, this study confirmed that ICoRD robustly extracts activation signals against noise, even within in vivo environments. ICoRD showed reliable detection from neurons with low SNR and sparse activation, which were not detected by the conventional methods. ICoRD will facilitate our understanding of neural circuit activity by providing significantly improved ROI detection from noisy images.

Fine Structural Changes in the Microvasculature of the Mouse Pinna: Aging and Radiation Injury

1974 ◽  
Vol 32 ◽  
pp. 54-55
Author(s):  
S. Phyllis Steamer ◽  
Rosemarie L. Devine
Keyword(s):  
Structural Changes ◽  
Radiation Treatment ◽  
Arterial Stenosis ◽  
Ultrastructural Changes ◽  
Vascular Effects ◽  
Microvascular Changes ◽  
Surrounding Connective Tissue ◽  
Fission Neutrons ◽  
Total Body

The importance of radiation damage to the skin and its vasculature was recognized by the early radiologists. In more recent studies, vascular effects were shown to involve the endothelium as well as the surrounding connective tissue. Microvascular changes in the mouse pinna were studied in vivo and recorded photographically over a period of 12-18 months. Radiation treatment at 110 days of age was total body exposure to either 240 rad fission neutrons or 855 rad 60Co gamma rays. After in vivo observations in control and irradiated mice, animals were sacrificed for examination of changes in vascular fine structure. Vessels were selected from regions of specific interest that had been identified on photomicrographs. Prominent ultrastructural changes can be attributed to aging as well as to radiation treatment. Of principal concern were determinations of ultrastructural changes associated with venous dilatations, segmental arterial stenosis and tortuosities of both veins and arteries, effects that had been identified on the basis of light microscopic observations. Tortuosities and irregularly dilated vein segments were related to both aging and radiation changes but arterial stenosis was observed only in irradiated animals.

Induction and Differentiation of Dental Epithelium in Vivo and in Vitro

1982 ◽  
Vol 40 ◽  
pp. 142-145
Author(s):  
E. J. Kollar
Keyword(s):  
Connective Tissue ◽  
Oral Mucosa ◽  
Basal Lamina ◽  
Functional Derivatives ◽  
Specific Source ◽  
Dental Epithelium ◽  
Connective Tissue Stroma

The differentiation and maintenance of many specialized epithelial structures are dependent on the underlying connective tissue stroma and on an intact basal lamina. These requirements are especially stringent in the development and maintenance of the skin and oral mucosa. The keratinization patterns of thin or thick cornified layers as well as the appearance of specialized functional derivatives such as hair and teeth can be correlated with the specific source of stroma which supports these differentiated expressions.

Ultrastructural Correlations between Clonal Human Tumor Colonies and in Vivo Neoplasms

1982 ◽  
Vol 40 ◽  
pp. 210-211
Author(s):  
M.J. Murphy ◽  
R.R. Price ◽  
J.C. Sloman
Keyword(s):  
Cultured Cells ◽  
Human Tumor ◽  
Cell Type ◽  
Tumor Mass ◽  
Initial Cell ◽  
Cloning Assay ◽  
Human Tumor Cloning ◽  
The Relationship

The in vitro human tumor cloning assay originally described by Salmon and Hamburger has been applied recently to the investigation of differential anti-tumor drug sensitivities over a broad range of human neoplasms. A major problem in the acceptance of this technique has been the question of the relationship between the cultured cells and the original patient tumor, i.e., whether the colonies that develop derive from the neoplasm or from some other cell type within the initial cell population. A study of the ultrastructural morphology of the cultured cells vs. patient tumor has therefore been undertaken to resolve this question. Direct correlation was assured by division of a common tumor mass at surgical resection, one biopsy being fixed for TEM studies, the second being rapidly transported to the laboratory for culture.

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